OpenRaiser/Intern-Atlas huggingface dataset:
On Optimal Online Algorithms for Energy Harvesting Systems with Continuous Energy and Data Arrivals
Venue: arXiv cs.IT 2017
Year: 2017

Energy harvesting (EH) has been developed to extend the lifetimes of energy-limited communication systems. In this letter, we consider a single-user EH communication system, in which both of the arrival data and the harvested energy curves are modeled as general functions. Unlike most of the works in the field, we investigate the online algorithms which only acquire the causal information of the arrival data and the harvested energy processes. We study how well the optimal online algorithm works compared with the optimal offline algorithm, and thus our goal is to find the lower and upper bounds for the ratio of the completion time in the optimal online algorithm to the optimal offline algorithm. We propose two online algorithms which achieve the upper bound of 2 on this ratio. Also, we show that this ratio is 2 for the optimal online algorithm.
Feasibility of Fog Computing
Venue: arXiv cs.DC 2017
Year: 2017

As billions of devices get connected to the Internet, it will not be sustainable to use the cloud as a centralised server. The way forward is to decentralise computations away from the cloud towards the edge of the network closer to the user. This reduces the latency of communication between a user device and the cloud, and is the premise of 'fog computing' defined in this paper. The aim of this paper is to highlight the feasibility and the benefits in improving the Quality-of-Service and Experience by using fog computing. For an online game use-case, we found that the average response time for a user is improved by 20% when using the edge of the network in comparison to using a cloud-only model. It was also observed that the volume of traffic between the edge and the cloud server is reduced by over 90% for the use-case. The preliminary results highlight the potential of fog computing in achieving a sustainable computing model and highlights the benefits of integrating the edge of the network into the computing ecosystem.
Quantifying Equivocation for Finite Blocklength Wiretap Codes
Venue: arXiv cs.IT 2017
Year: 2017

This paper presents a new technique for providing the analysis and comparison of wiretap codes in the small blocklength regime over the binary erasure wiretap channel. A major result is the development of Monte Carlo strategies for quantifying a code's equivocation, which mirrors techniques used to analyze normal error correcting codes. For this paper, we limit our analysis to coset-based wiretap codes, and make several comparisons of different code families at small and medium blocklengths. Our results indicate that there are security advantages to using specific codes when using small to medium blocklengths.
Learning first-order definable concepts over structures of small degree
Venue: arXiv cs.LG 2017
Year: 2017

We consider a declarative framework for machine learning where concepts and hypotheses are defined by formulas of a logic over some background structure. We show that within this framework, concepts defined by first-order formulas over a background structure of at most polylogarithmic degree can be learned in polylogarithmic time in the "probably approximately correct" learning sense.
An Integrated Design of Optimization and Physical Dynamics for Energy Efficient Buildings: A Passivity Approach
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we address energy management for heating, ventilation, and air-conditioning (HVAC) systems in buildings, and present a novel combined optimization and control approach. We first formulate a thermal dynamics and an associated optimization problem. An optimization dynamics is then designed based on a standard primal-dual algorithm, and its strict passivity is proved. We then design a local controller and prove that the physical dynamics with the controller is ensured to be passivity-short. Based on these passivity results, we interconnect the optimization and physical dynamics, and prove convergence of the room temperatures to the optimal ones defined for unmeasurable disturbances. Finally, we demonstrate the present algorithms through simulation.
Leveraging Cognitive Features for Sentiment Analysis
Venue: arXiv cs.CL 2017
Year: 2017

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.
Universal Audio Steganalysis Based on Calibration and Reversed Frequency Resolution of Human Auditory System
Venue: arXiv cs.MM 2017
Year: 2017

Calibration and higher order statistics (HOS) are standard components of many image steganalysis systems. These techniques have not yet found adequate attention in audio steganalysis context. Specifically, most of current works are either non-calibrated or only based on noise removal approach. This paper aims to fill these gaps by proposing a new set of calibrated features based on re-embedding technique. Additionally, we show that least significant bit (LSB) is the most sensitive bit-plane to data hiding algorithms and therefore it can be employed as a universal embedding method. Furthermore, the proposed features are based on a model that has the maximum deviation from human auditory system (HAS), and therefore are more suitable for the purpose of steganalysis. Performance of the proposed method is evaluated on a wide range of data hiding algorithms in both targeted and universal paradigms. Simulation results show that the proposed method can detect the finest traces of data hiding algorithms and in very low embedding rates. The system detects steghide at capacity of 0.06 bit per symbol (BPS) with sensitivity of 98.6% (music) and 78.5% (speech). These figures are respectively 7.1% and 27.5% higher than state-of-the-art results based on RMFCC.
Parametricity, automorphisms of the universe, and excluded middle
Venue: arXiv cs.LO 2017
Year: 2017

It is known that one can construct non-parametric functions by assuming classical axioms. Our work is a converse to that: we prove classical axioms in dependent type theory assuming specific instances of non-parametricity. We also address the interaction between classical axioms and the existence of automorphisms of a type universe. We work over intensional Martin-Löf dependent type theory, and in some results assume further principles including function extensionality, propositional extensionality, propositional truncation, and the univalence axiom.
Strong isomorphism in Eisert-Wilkens-Lewenstein type quantum games
Venue: arXiv cs.GT 2017
Year: 2017

The aim of this paper is to bring together the notions of quantum game and game isomorphism. The work is intended as an attempt to introduce a new criterion for quantum game schemes. The generally accepted requirement forces a quantum scheme to generate the classical game in a particular case. Now, given a quantum game scheme and two isomorphic classical games, we additionally require the resulting quantum games to be isomorphic as well. We are concerned with the Eisert-Wilkens-Lewenstein quantum game scheme and the strong isomorphism between games in strategic form.
Demand-Driven Pointer Analysis with Strong Updates via Value-Flow Refinement
Venue: arXiv cs.PL 2017
Year: 2017

We present a new demand-driven flow- and context-sensitive pointer analysis with strong updates for C programs, called SUPA, that enables computing points-to information via value-flow refinement, in environments with small time and memory budgets such as IDEs. We formulate SUPA by solving a graph reachability problem on an inter-procedural value-flow graph representing a program's def-use chains, which are pre-computed efficiently but over-approximately. To answer a client query (a request for a variable's points-to set), SUPA reasons about the flow of values along the pre-computed def-use chains sparsely (rather than across all program points), by performing only the work necessary for the query (rather than analyzing the whole program). In particular, strong updates are performed to filter out spurious def-use chains through value-flow refinement as long as the total budget is not exhausted. SUPA facilitates efficiency and precision tradeoffs by applying different pointer analyses in a hybrid multi-stage analysis framework. We have implemented SUPA in LLVM (3.5.0) and evaluate it by choosing uninitialized pointer detection as a major client on 18 open-source C programs. As the analysis budget increases, SUPA achieves improved precision, with its single-stage flow-sensitive analysis reaching 97.4% of that achieved by whole-program flow-sensitive analysis by consuming about 0.18 seconds and 65KB of memory per query, on average (with a budget of at most 10000 value-flow edges per query). With context-sensitivity also considered, SUPA's two- stage analysis becomes more precise for some programs but also incurs more analysis times. SUPA is also amenable to parallelization. A parallel implementation of its single-stage flow-sensitive analysis achieves a speedup of up to 6.9x with an average of 3.05x a 8-core machine with respect its sequential version.
Rigorous Dynamics of Expectation-Propagation-Based Signal Recovery from Unitarily Invariant Measurements
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates sparse signal recovery based on expectation propagation (EP) from unitarily invariant measurements. A rigorous analysis is presented for the state evolution (SE) of an EP-based message-passing algorithm in the large system limit, where both input and output dimensions tend to infinity at an identical speed. The main result is the justification of an SE formula conjectured by Ma and Ping.
Logical Inferences with Contexts of RDF Triples
Venue: arXiv cs.AI 2017
Year: 2017

Logical inference, an integral feature of the Semantic Web, is the process of deriving new triples by applying entailment rules on knowledge bases. The entailment rules are determined by the model-theoretic semantics. Incorporating context of an RDF triple (e.g., provenance, time, and location) into the inferencing process requires the formal semantics to be capable of describing the context of RDF triples also in the form of triples, or in other words, RDF contextual triples about triples. The formal semantics should also provide the rules that could entail new contextual triples about triples. In this paper, we propose the first inferencing mechanism that allows context of RDF triples, represented in the form of RDF triples about triples, to be the first-class citizens in the model-theoretic semantics and in the logical rules. Our inference mechanism is well-formalized with all new concepts being captured in the model-theoretic semantics. This formal semantics also allows us to derive a new set of entailment rules that could entail new contextual triples about triples. To demonstrate the feasibility and the scalability of the proposed mechanism, we implement a new tool in which we transform the existing knowledge bases to our representation of RDF triples about triples and provide the option for this tool to compute the inferred triples for the proposed rules. We evaluate the computation of the proposed rules on a large scale using various real-world knowledge bases such as Bio2RDF NCBI Genes and DBpedia. The results show that the computation of the inferred triples can be highly scalable. On average, one billion inferred triples adds 5-6 minutes to the overall transformation process. NCBI Genes, with 20 billion triples in total, took only 232 minutes for the transformation of 12 billion triples and added 42 minutes for inferring 8 billion triples to the overall process.
Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
Venue: arXiv cs.SD 2017
Year: 2017

This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on sound event detection. We recorded the sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental sound data to remedy the scarcity of drone sound data in diverse environments. We investigated the effectiveness of state-of-the-art event sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone sound detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time detection systems.
Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)
Venue: arXiv cs.NE 2017
Year: 2017

In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture. Especially, we perform fusion of DSM and IRRG optical data on the ISPRS Vaihingen dataset over a urban area and obtain new state-of-the-art results.
Exploiting the Cloud Control Plane for Fun and Profit
Venue: arXiv cs.DC 2017
Year: 2017

Cloud providers typically charge for their services. There are diverse pricing models which often follow a pay-per-use paradigm. The consumers' payments are expected to cover all cost which incurs to the provider for processing, storage, bandwidth, data centre operation and engineering efforts, among others. In contrast, the consumer management interfaces are free of charge as they are expected to cause only a minority of the load compared to the actual computing services. With new service models and more complex and powerful management abilities, it is time to rethink this decision. The paper shows how to exploit the control plane of AWS Lambda to implement stateful services practically for free and under some circumstances even guaranteed for free which if widely deployed would cause a monetary loss for the provider. It also elaborates on the consistency model for AWS Lambda.
Computation Diversity in Emerging Networking Paradigms
Venue: arXiv cs.NI 2017
Year: 2017

Nowadays, computation is playing an increasingly more important role in the future generation of computer and communication networks, as exemplified by the recent progress in software defined networking (SDN) for wired networks as well as cloud radio access networks (C-RAN) and mobile cloud computing (MCC) for wireless networks. This paper proposes a unified concept, i.e., computation diversity, to describe the impact and diverse forms of the computation resources on both wired and wireless communications. By linking the computation resources to the communication networks based on quality of service (QoS) requirements, we can show how computation resources influence the networks. Moreover, by analyzing the different functionalities of computation resources in SDN, C-RAN, and MCC, we can show diverse and flexible form that the computation resources present in different networks. The study of computation diversity can provide guidance to the future networks design, i.e., how to allocate the resources jointly between computation (e.g., CPU capacity) and communication (e.g., bandwidth), and thereby saving system energy and increase users' experiences.
The exponential family of Markov chains and its information geometry
Venue: arXiv cs.IT 2017
Year: 2017

We introduce a new definition of exponential family of Markov chains, and show that many characteristic properties of the usual exponential family of probability distributions are properly extended to Markov chains. The method of information geometry is effectively applied to our framework, which enables us to characterize the divergence rate of Markov chain from a differential geometric viewpoint.
Effective and Extensible Feature Extraction Method Using Genetic Algorithm-Based Frequency-Domain Feature Search for Epileptic EEG Multi-classification
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, a genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of inter-class distance and intra-class distance. Moreover, the proposed feature search method can additionally search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable, thus, GAFDS exhibits good extensibility. Multiple classic classifiers (i.e., $k$-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve good results by using the features generated by GAFDS method and the optimized selection. Specifically, the accuracies for the two-classification and three-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in feature extraction for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.
Dual Sub-6 GHz -- Millimeter Wave Beamforming and Communications to Achieve Low Latency and High Energy Efficiency in 5G Systems
Venue: arXiv cs.IT 2017
Year: 2017

We propose a hybrid architecture that integrates RF (i.e., sub-6 GHz) and millimeter wave (mmWave) technologies for 5G cellular systems. In particular, communications in the mmWave band faces significant challenges due to variable channels, intermittent connectivity, and high energy usage. On the other hand, speeds for electronic processing of data is of the same order as typical rates for mmWave interfaces which makes the use of complex algorithms for tracking channel variations and adjusting resources accordingly impractical. Our proposed architecture integrates the RF and mmWave interfaces for beamforming and data transfer, and exploits the spatio-temporal correlations between the interfaces. Based on extensive experimentation in indoor and outdoor settings, we demonstrate that an integrated RF/mmWave signaling and channel estimation scheme can remedy the problem of high energy usage and delay associated with mmWave beamforming. In addition, cooperation between two interfaces at the higher layers effectively addresses the high delays caused by highly intermittent mmWave connectivity. We design a scheduler that fully exploits the mmWave bandwidth, while the RF link acts as a fallback mechanism to prevent high delay. To this end, we formulate an optimal scheduling problem over the RF and mmWave interfaces where the goal is to maximize the delay-constrained throughput of the mmWave interface. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations.
Distributed Decoding of Convolutional Network Error Correction Codes
Venue: arXiv cs.IT 2017
Year: 2017

A Viterbi-like decoding algorithm is proposed in this paper for generalized convolutional network error correction coding. Different from classical Viterbi algorithm, our decoding algorithm is based on minimum error weight rather than the shortest Hamming distance between received and sent sequences. Network errors may disperse or neutralize due to network transmission and convolutional network coding. Therefore, classical decoding algorithm cannot be employed any more. Source decoding was proposed by multiplying the inverse of network transmission matrix, where the inverse is hard to compute. Starting from the Maximum A Posteriori (MAP) decoding criterion, we find that it is equivalent to the minimum error weight under our model. Inspired by Viterbi algorithm, we propose a Viterbi-like decoding algorithm based on minimum error weight of combined error vectors, which can be carried out directly at sink nodes and can correct any network errors within the capability of convolutional network error correction codes (CNECC). Under certain situations, the proposed algorithm can realize the distributed decoding of CNECC.
A Practical Approach for Successive Omniscience
Venue: arXiv cs.IT 2017
Year: 2017

The system that we study in this paper contains a set of users that observe a discrete memoryless multiple source and communicate via noise-free channels with the aim of attaining omniscience, the state that all users recover the entire multiple source. We adopt the concept of successive omniscience (SO), i.e., letting the local omniscience in some user subset be attained before the global omniscience in the entire system, and consider the problem of how to efficiently attain omniscience in a successive manner. Based on the existing results on SO, we propose a CompSetSO algorithm for determining a complimentary set, a user subset in which the local omniscience can be attained first without increasing the sum-rate, the total number of communications, for the global omniscience. We also derive a sufficient condition for a user subset to be complimentary so that running the CompSetSO algorithm only requires a lower bound, instead of the exact value, of the minimum sum-rate for attaining global omniscience. The CompSetSO algorithm returns a complimentary user subset in polynomial time. We show by example how to recursively apply the CompSetSO algorithm so that the global omniscience can be attained by multi-stages of SO.
Delivery Latency Trade-Offs of Heterogeneous Contents in Fog Radio Access Networks
Venue: arXiv cs.IT 2017
Year: 2017

A Fog Radio Access Network (F-RAN) is a cellular wireless system that enables content delivery via the caching of popular content at edge nodes (ENs) and cloud processing. The existing information-theoretic analyses of F-RAN systems, and special cases thereof, make the assumption that all requests should be guaranteed the same delivery latency, which results in identical latency for all files in the content library. In practice, however, contents may have heterogeneous timeliness requirements depending on the applications that operate on them. Given per-EN cache capacity constraint, there exists a fundamental trade-off among the delivery latencies of different users' requests, since contents that are allocated more cache space generally enjoy lower delivery latencies. For the case with two ENs and two users, the optimal latency trade-off is characterized in the high-SNR regime in terms of the Normalized Delivery Time (NDT) metric. The main results are illustrated by numerical examples.